Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells

  1. Moosung Lee
  2. Young-Ho Lee
  3. Jinyeop Song
  4. Geon Kim
  5. YoungJu Jo
  6. HyunSeok Min
  7. Chan Hyuk Kim  Is a corresponding author
  8. YongKeun Park  Is a corresponding author
  1. Korea Advanced Institute of Science and Technology, Republic of Korea
  2. Tomocube Inc, Republic of Korea

Abstract

The immunological synapse (IS) is a cell-cell junction between a T cell and a professional antigen-presenting cell. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniques, most of which rely on fluorescence microscopy, have been used to study the dynamics of IS. However, the inherent limitations associated with the fluorescence-based imaging, such as photo-bleaching and photo-toxicity, prevent the long-term assessment of dynamic changes of IS with high frequency. Here, we propose and experimentally validate a label-free, volumetric, and automated assessment method for IS dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed method enables an automatic and quantitative spatiotemporal analysis of IS kinetics of morphological and biochemical parameters associated with IS dynamics, providing a new option for immunological research.

Data availability

We have provided pre-processing and post-processing codes, and training and validation datasets used in Figure 3-Video 1 (https://osf.io/9w32p/). Also, the Unet architecture code is available in https://github.com/JinyeopSong/190124_CART-Segmentation-best.

The following data sets were generated

Article and author information

Author details

  1. Moosung Lee

    Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
    Competing interests
    Moosung Lee, Mr. Moosung Lee has financial interests in Tomocube Inc., a company that commercializes optical diffraction tomography and quantitative phase-imaging instruments, and is one of the sponsors of the work..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2826-5401
  2. Young-Ho Lee

    Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
    Competing interests
    Young-Ho Lee, Dr. Y.H. Lee is an employee of Curocell Inc.
  3. Jinyeop Song

    Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
    Competing interests
    No competing interests declared.
  4. Geon Kim

    Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
    Competing interests
    No competing interests declared.
  5. YoungJu Jo

    Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
    Competing interests
    No competing interests declared.
  6. HyunSeok Min

    Tomocube Inc, Daejeon, Republic of Korea
    Competing interests
    No competing interests declared.
  7. Chan Hyuk Kim

    Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
    For correspondence
    kimchanhyuk@kaist.ac.kr
    Competing interests
    Chan Hyuk Kim, Prof. C. H. K. is a co-founder and shareholder of Curocell inc...
  8. YongKeun Park

    Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
    For correspondence
    yk.park@kaist.ac.kr
    Competing interests
    YongKeun Park, Prof. Park has financial interests in Tomocube Inc., a company that commercializes optical diffraction tomography and quantitative phase-imaging instruments, and is one of the sponsors of the work.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0528-6661

Funding

National Research Foundation of Korea (2017M3C1A3013923)

  • Moosung Lee
  • Jinyeop Song
  • Geon Kim
  • YongKeun Park

National Research Foundation of Korea (2015R1A3A2066550)

  • Moosung Lee
  • Jinyeop Song
  • Geon Kim
  • YongKeun Park

National Research Foundation of Korea (2018K000396)

  • Moosung Lee
  • Jinyeop Song
  • Geon Kim
  • YongKeun Park

The Ministry of Science and ICT (2014M3A9D8032525)

  • Young-Ho Lee
  • Chan Hyuk Kim

The Ministry of Science and ICT (N11190028)

  • Young-Ho Lee
  • Chan Hyuk Kim

National Research Foundation of Korea (2019R1A2C1004129)

  • Young-Ho Lee
  • Chan Hyuk Kim

The funders had no role in study design, data collection, interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Michael L Dustin, University of Oxford, United Kingdom

Version history

  1. Received: June 4, 2019
  2. Accepted: December 16, 2020
  3. Accepted Manuscript published: December 17, 2020 (version 1)
  4. Version of Record published: January 20, 2021 (version 2)

Copyright

© 2020, Lee et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 6,962
    views
  • 827
    downloads
  • 49
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Moosung Lee
  2. Young-Ho Lee
  3. Jinyeop Song
  4. Geon Kim
  5. YoungJu Jo
  6. HyunSeok Min
  7. Chan Hyuk Kim
  8. YongKeun Park
(2020)
Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells
eLife 9:e49023.
https://doi.org/10.7554/eLife.49023

Share this article

https://doi.org/10.7554/eLife.49023

Further reading

    1. Cell Biology
    2. Immunology and Inflammation
    Kevin Portmann, Aline Linder, Klaus Eyer
    Research Article

    Cytokine polyfunctionality is a well-established concept in immune cells, especially T cells, and their ability to concurrently produce multiple cytokines has been associated with better immunological disease control and subsequent effectiveness during infection and disease. To date, only little is known about the secretion dynamics of those cells, masked by the widespread deployment of mainly time-integrated endpoint measurement techniques that do not easily differentiate between concurrent and sequential secretion. Here, we employed a single-cell microfluidic platform capable of resolving the secretion dynamics of individual PBMCs. To study the dynamics of poly-cytokine secretion, as well as the dynamics of concurrent and sequential polyfunctionality, we analyzed the response at different time points after ex vivo activation. First, we observed the simultaneous secretion of cytokines over the measurement time for most stimulants in a subpopulation of cells only. Second, polyfunctionality generally decreased with prolonged stimulation times and revealed no correlation with the concentration of secreted cytokines in response to stimulation. However, we observed a general trend towards higher cytokine secretion in polyfunctional cells, with their secretion dynamics being distinctly different from mono-cytokine-secreting cells. This study provided insights into the distinct secretion behavior of heterogenous cell populations after stimulation with well-described agents and such a system could provide a better understanding of various immune dynamics in therapy and disease.

    1. Cell Biology
    2. Neuroscience
    Toshiharu Ichinose, Shu Kondo ... Hiromu Tanimoto
    Research Article

    Multicellular organisms are composed of specialized cell types with distinct proteomes. While recent advances in single-cell transcriptome analyses have revealed differential expression of mRNAs, cellular diversity in translational profiles remains underinvestigated. By performing RNA-seq and Ribo-seq in genetically defined cells in the Drosophila brain, we here revealed substantial post-transcriptional regulations that augment the cell-type distinctions at the level of protein expression. Specifically, we found that translational efficiency of proteins fundamental to neuronal functions, such as ion channels and neurotransmitter receptors, was maintained low in glia, leading to their preferential translation in neurons. Notably, distribution of ribosome footprints on these mRNAs exhibited a remarkable bias toward the 5′ leaders in glia. Using transgenic reporter strains, we provide evidence that the small upstream open-reading frames in the 5’ leader confer selective translational suppression in glia. Overall, these findings underscore the profound impact of translational regulation in shaping the proteomics for cell-type distinction and provide new insights into the molecular mechanisms driving cell-type diversity.